Several references are identified by numerals in parenthesis in this patent specification, and are hereby incorporated by reference. The full citations are listed at the end of the specification. Reference [18] is a paper by the inventor and a person working under his direction regarding the subject matter of the paper.
Real-time MR imaging could exert a profound influence on neuroscience in the future by enabling the direct visualization of neuronal interactions. At this time, however, the known practical embodiments of MRI require at least some degree of gradient encoding, and this in turn sets a lower limit of about 100 ms for volume acquisition.
In the original formulation of MRI by Lauterbur [1], spatial encoding is achieved by applying successive magnetic field gradients to the imaging volume. Each new gradient is associated with a different radiofrequency (RF) excitation, and RF-induced echoes form a line in k-space, discretized into N elements, where N is the dimension of the image matrix. After N progressively increasing gradients, and N echoes, there are N lines in k-space, and the N×N k-space matrix is subjected to a 2-D Fourier transform, rendering the N×N image matrix. In echoplanar imaging (EPI) Mansfield [2] showed that spatial encoding could be achieved by means of trains of gradient reversals after a single RF excitation. An advantage of using gradient reversals is speed; a disadvantage is sensitivity to susceptibility changes and magnetic field inhomogeneities.
More recently studied parallel MRI (pMRI) uses the spatial sensitivities of multiple receiver coils (detectors) arranged around the object for spatial encoding of voxels in a single slice, thus reducing the need for gradient reversals and RF pulses [3, 4]. pMRI images are discussed by Kelton, Magin and Wright [5], and by Ra and Rim [6]. pMRI modifies the Lauterbur-Mansfield approach to include multiple receiver coils, so that if the number of coils is n, then the number of gradient reversals is a factor n times smaller. According to such studies of pMRI, many of the conventional 180° RF pulses can be replaced by short trains of gradient reversals, with an acceptable change in image quality. Thus, pMRI can embrace some of the advantages of EPI, without some of the disadvantages. Because gradient switching is generally several times faster than RF excitations, image quality can be adequately maintained while speed is increased somewhat.
The pMRI initially proposed by Hutchinson [3,4] is for single shot imaging that ignores sources of noise in the surface coils. Subsequent analyses of detector arrays by Roemer [7] and Ocali [8] account to some extent for the effects of noise, with algorithms for conventional gradient encoding. A concept of “ultimate signal-to-noise,” [8] can be considered, by which was meant that, at least for gradient encoding, large numbers of small receiver coils—when properly configured—can be more efficient than a single receiver. Sodickson [9], with SMASH, and Pruessmann [10], with SENSE, proposed merging gradient-encoding and spatial sensitivity encoding. These merging techniques can be designated “hybrid” since they use both methods of encoding.
Merging of the two fundamental ways of encoding comes at a considerable price, because for the hybrid techniques ultimate signal-to-noise is reached not with large numbers of coils, but with small numbers (typically 4). Signal-to-noise (SNR) in hybrid pMRI is reduced by 3 sources: (i) electrical noise, (ii) reduced numbers of echoes, and (iii) a critical geometric factor, g, as discussed by Ohliger [11] and Wiesiger [12]. Sources of electrical noise include preamplifiers, coupling between adjacent coils, and body thermal noise. In addition, there is noise from the eddy currents in the body surface due not only to the gradient reversals but also to re-radiation of signal by the coil, which is both a receiver and a dipole radiator. These sources of noise can be reduced, however, and for conventional gradient encoding multiple small coils have been shown to be more efficient than one large coil [7,8] so there is no insurmountable noise problem inherent to small coils. When multiple coils are used to encode, however, there can be an additional problem due to the inefficiency of spatial sensitivity encoding, compared with gradient encoding, when the coils are large. This in turn is due to the slow spatial variations of the spatial sensitivity of each coil. In addition, since there are fewer phase-encoding steps SNR is further reduced. The signal-to-noise ratio is now given by [11,12]:SNR=SNRFull/(√R)g  (1)where SNRFull is the SNR for gradient-only encoding, R is the acceleration factor (in this case the number of detectors) and g is the geometric factor. The loss factor √R represents the reduced SNR due to the reduced number of phase encoding steps, and could not be mitigated absent phase encoding. The g-factor is intrinsic to the geometry of the coil array, and is a measure of the capacity of the array to compensate for reductions in gradient encoding. For very small numbers of detectors g is close to 1, but as the number of coils increases, and the number of phase-encoding steps correspondingly decreases, g suddenly becomes large and the images are degraded.
It has generally been accepted that for practical purposes this single limitation means that for hybrid techniques acceleration factors need to be at most about 4, so that the rate of acquisition of SNR is about double that of non-parallel techniques. The reason g>1 with increasing accelerations is that the transformations used to obtain the image in conventional pMRI are non-unitary, and the reason for this is that the solutions to Maxwell's equations are “smooth,” by which is meant that for large detectors the spatial dependence of the radio field is not as granular as the spatial dependence of the gradients used for conventional encoding. This in turn means that large groups of adjacent pixels may have similar spatial sensitivity profiles.
As R increases, and the number of gradient-encoding steps correspondingly decreases, more and more of the burden of encoding is shouldered by the receiver array. For R=N=4, only 25% of the encoding is gradient-based. With more detectors there is an unsupportable reliance on spatial sensitivity encoding and this leads to rapid image degradation when R>4. The spatial sensitivity maps can be modeled for illustrative purposes by taking, as an approximation to the solutions of Maxwell's equations, only the 1/r3 dependence of the near field dipole. This view of an inherent limitation of all constructions of parallel MRI has been challenged by Keil and Wald [13], with a theoretical evaluation suggesting that the spatial sensitivity of small coils could contribute more in spatial encoding in hybrid pMRI while still using some gradient encoding. The original concept for single shot, single slice imaging free of gradient reversals [3,4] was tested several years ago by McDougall and Wright [14], using a 64 channel coil with an acceleration factor of 64. However, this was not for volume encoding but only for a single slice. Proposals have been published to employ static magnetic field gradients produced by thin magnetic films to encode flow [15], to use two or three RF phase gradients in an arrangement free of magnetic gradients [16], and to use a flexible array coils populated with multiple coils in multi-slice-multi-echo sequences that inherently rely on magnetic field gradients [17].
The prior commercial embodiments of pMRI known to the inventor herein require a plurality of magnetic gradients and/or gradient reversals. This can generate a large amount of noise, first RF and other electrical noise, and second audible noise. This means that image signal-to-noise ratio is lowered, and that the machine can be extremely loud (up to 120 dB).
This patent specification recognizes and addresses these and other aspects of prior MRI work by providing a new, radically different approach.